Glioblastoma multiforme (GBM) is the most common primary brain tumor in adults. GBM is characterized by a high proliferative rate and aggressive invasiveness within the brain. Patients diagnosed with GBM may be subjected to aggressive multimodal treatment including surgery, radiotherapy, or chemotherapy. Despite aggressive multimodal treatment, the median survival time after diagnosis for GBM patients ranges from ten to fourteen months. However, individual outcomes are very heterogeneous, with five percent to ten percent of patients diagnosed with GBM being long-term survivors who survive for more than three years.
Within the confined environment of the brain vault, tumor growth, especially in aggressive cancers such as GBM, forces the compression of surrounding brain tissue. This compression results in increased intracranial pressure, exacerbation of vasogenic edema, and brain herniation. Herniation or gross distortion of the brain stem is the cause of death in approximately 60% of GBM cases.
Some conventional approaches to predicting GBM survival times attempt to identify prognostic markers, including tumor size, location, age, or Karnofsky performance scores (KPS). Other approaches attempt to associate molecular markers with survival time. While some prognostic markers have been identified as indicative of GBM survival time, outcomes remain heterogeneous. Furthermore, molecular heterogeneity within GBM tissue represents a challenge to the development of targeted GBM therapies.
Conventional radiomic analysis approaches to predicting GBM survival time have confined their analysis to within GBM tumor or peri-tumoral areas. Other conventional approaches for predicting GBM survival time attempt to identify a relationship between the extent of tumor mass effect as manifested on magnetic resonance imaging (MRI) images and overall survival time. These conventional approaches use 2-dimensional (2D) distance-to-the-midline measurements to capture midline shift due to mass effect. However, conventional approaches that use simplistic 2D distance-to-the-midline measurements provide conflicting and ambiguous results. Since radiologists may be challenged to reliably distinguish long-term survivors from short-term survivors using conventional approaches in clinically relevant time frames, patients may be subjected to sub-optimal treatments. These treatments may take time, cost money, and put a patient at additional risk.